The Bianconi–Barabási model is a model in network science that explains the growth of complex evolving networks. This model can explain that nodes with different characteristics acquire links at different rates. It predicts that a node's growth depends on its fitness and can calculate the degree distribution. The Bianconi–Barabási model [1] [2] is named after its inventors Ginestra Bianconi and Albert-László Barabási. This model is a variant of the Barabási–Albert model. The model can be mapped to a Bose gas and this mapping can predict a topological phase transition between a "rich-get-richer" phase and a "winner-takes-all" phase.[2]
The Barabási–Albert (BA) model uses two concepts: growth and preferential attachment. Here, growth indicates the increase in the number of nodes in the network with time, and preferential attachment means that more connected nodes receive more links. The Bianconi–Barabási model,[1] on top of these two concepts, uses another new concept called the fitness. This model makes use of an analogy with evolutionary models. It assigns an intrinsic fitness value to each node, which embodies all the properties other than the degree.[3] The higher the fitness, the higher the probability of attracting new edges. Fitness can be defined as the ability to attract new links – "a quantitative measure of a node's ability to stay in front of the competition".[4]
While the Barabási–Albert (BA) model explains the "first mover advantage" phenomenon, the Bianconi–Barabási model explains how latecomers also can win. In a network where fitness is an attribute, a node with higher fitness will acquire links at a higher rate than less fit nodes. This model explains that age is not the best predictor of a node's success, rather latecomers also have the chance to attract links to become a hub.
The Bianconi–Barabási model can reproduce the degree correlations of the Internet Autonomous Systems.[5] This model can also show condensation phase transitions in the evolution of complex network.[6] [2] The BB model can predict the topological properties of Internet.[7]
The fitness network begins with a fixed number of interconnected nodes. They have different fitness, which can be described with fitness parameter,
ηj
\rho(η)
The assumption here is that a node’s fitness is independent of time, and is fixed. A new node j with m links and a fitness
ηj
The probability
\Pii
i
ki
ηi
i
\Pii=
| ηiki | |
| \sumjηjkj |
.
Each node’s evolution with time can be predicted using the continuum theory. If initial number of node is
m
i
| \partialki | |
| \partialt |
=m
| ηiki | |
| \sumjηjkj |
Assuming the evolution of
ki
\beta(ηi)
k(t,ti,ηi)=m\left(
| t | |
| ti |
| \beta(ηi) | |
| \right) |
where
ti
i
Here,
\beta(η)=
| η | |
| C |
andC=\int\rho(η)
| η | |
| 1-\beta(η) |
dη.
If all fitnesses are equal in a fitness network, the Bianconi–Barabási model reduces to the Barabási–Albert model, when the degree is not considered, the model reduces to the fitness model (network theory).
When fitnesses are equal, the probability
\Pii
i
ki
i
\Pii=
| ki | |
| \sumjkj |
.
Degree distribution of the Bianconi–Barabási model depends on the fitness distribution
\rho(η)
There are various statistical methods to measure node fitnesses
ηi
\rho(η)
The Bianconi–Barabási model has been extended to weighted networks [11] displaying linear and superlinear scaling of the strength with the degree of the nodes as observed in real network data.[12] This weighted model can lead to condensation of the weights of the network when few links acquire a finite fraction of the weight of the entire network.[11] Recently it has been shown that the Bianconi–Barabási model can be interpreted as a limit case of the model for emergent hyperbolic network geometry [13] called Network Geometry with Flavor.[14] The Bianconi–Barabási model can be also modified to study static networks where the number of nodes is fixed.[15]
Bose–Einstein condensation in networks is a phase transition observed in complex networks that can be described by the Bianconi–Barabási model.[1] This phase transition predicts a "winner-takes-all" phenomena in complex networks and can be mathematically mapped to the mathematical model explaining Bose–Einstein condensation in physics.
In physics, a Bose–Einstein condensate is a state of matter that occurs in certain gases at very low temperatures. Any elementary particle, atom, or molecule, can be classified as one of two types: a boson or a fermion. For example, an electron is a fermion, while a photon or a helium atom is a boson. In quantum mechanics, the energy of a (bound) particle is limited to a set of discrete values, called energy levels. An important characteristic of a fermion is that it obeys the Pauli exclusion principle, which states that no two fermions may occupy the same state. Bosons, on the other hand, do not obey the exclusion principle, and any number can exist in the same state. As a result, at very low energies (or temperatures), a great majority of the bosons in a Bose gas can be crowded into the lowest energy state, creating a Bose–Einstein condensate.
Bose and Einstein have established that the statistical properties of a Bose gas are governed by the Bose–Einstein statistics. In Bose–Einstein statistics, any number of identical bosons can be in the same state. In particular, given an energy state, the number of non-interacting bosons in thermal equilibrium at temperature is given by the Bose occupation number
| n(\varepsilon)= | 1 |
| e\beta(\varepsilon-\mu)-1 |
where the constant is determined by an equation describing the conservation of the number of particles
N=\intd\varepsilong(\varepsilon)n(\varepsilon)
with being the density of states of the system.
This last equation may lack a solution at low enough temperatures when for . In this case a critical temperature is found such that for the system is in a Bose-Einstein condensed phase and a finite fraction of the bosons are in the ground state.
The density of states depends on the dimensionality of the space. In particular
g(\varepsilon)\sim
| ||||
| \varepsilon |
The evolution of many complex systems, including the World Wide Web, business, and citation networks, is encoded in the dynamic web describing the interactions between the system’s constituents. The evolution of these networks is captured by the Bianconi-Barabási model, which includes two main characteristics of growing networks: their constant growth by the addition of new nodes and links and the heterogeneous ability of each node to acquire new links described by the node fitness. Therefore the model is also known as fitness model.Despite their irreversible and nonequilibrium nature, these networks follow the Bose statistics and can be mapped to a Bose gas.In this mapping, each node is mapped to an energy state determined by its fitness and each new link attached to a given node is mapped to a Bose particle occupying the corresponding energy state. This mapping predicts that the Bianconi–Barabási model can undergo a topological phase transition in correspondence to the Bose–Einstein condensation of the Bose gas. This phase transition is therefore called Bose-Einstein condensation in complex networks.Consequently addressing the dynamical properties of these nonequilibrium systems within the framework of equilibrium quantum gases predicts that the “first-mover-advantage,” “fit-get-rich (FGR),” and “winner-takes-all” phenomena observed in a competitive systems are thermodynamically distinct phases of the underlying evolving networks.[2]
Starting from the Bianconi-Barabási model, the mapping of a Bose gas to a network can be done by assigning an energy to each node, determined by its fitness through the relation[2] [16]
| \varepsilon | ||||
|
ln{ηi}
where . In particular when all the nodes have equal fitness, when instead nodes with different "energy" have very different fitness. We assume that the network evolves through a modified preferential attachment mechanism. At each time a new node with energy drawn from a probability distribution enters in the network and attach a new link to a node chosen with probability:
| \Pi | ||||||||||||||||||||
|
.
In the mapping to a Bose gas, we assign to every new link linked by preferential attachment to node a particle in the energy state .
The continuum theory predicts that the rate at which links accumulate on node with "energy" is given by
| \partialki(\varepsiloni,t,ti) | =m | |
| \partialt |
| |||||||
| Zt |
where
ki(\varepsiloni,t,ti)
ti
Zt
Zt=\sumi
| -\beta\varepsiloni | |
| e |
ki(\varepsiloni,t,ti).
The solution of this differential equation is:
ki(\varepsiloni,t,t
|
| f(\varepsiloni) | |
| \right) |
where the dynamic exponent
f(\varepsilon)
f(\varepsilon)=e-\beta(\varepsilon-\mu)
\intd\varepsilonp(\varepsilon)
| 1 | |
| e\beta(\varepsilon-\mu)-1 |
=1
where is the probability that a node has "energy" and "fitness" . In the limit,, the occupation number, giving the number of links linked to nodes with "energy", follows the familiar Bose statistics
| n(\varepsilon)= | 1 |
| e\beta(\varepsilon-1 |
.
The definition of the constant in the network models is surprisingly similar to the definition of the chemical potential in a Bose gas. In particular for probabilities such that for at high enough value of we have a condensation phase transition in the network model. When this occurs, one node, the one with higher fitness acquires a finite fraction of all the links. The Bose–Einstein condensation in complex networks is, therefore, a topological phase transition after which the network has a star-like dominant structure.
The mapping of a Bose gas predicts the existence of two distinct phases as a function of the energy distribution. In the fit-get-rich phase, describing the case of uniform fitness, the fitter nodes acquire edges at a higher rate than older but less fit nodes. In the end the fittest node will have the most edges, but the richest node is not the absolute winner, since its share of the edges (i.e. the ratio of its edges to the total number of edges in the system) reduces to zero in the limit of large system sizes (Fig.2(b)). The unexpected outcome of this mapping is the possibility of Bose–Einstein condensation for, when the fittest node acquires a finite fraction of the edges and maintains this share of edges over time (Fig.2(c)).
\rho(η)
\rho(η)=(λ+1)(1-η)λ,
where
λ=1
However, the existence of the Bose–Einstein condensation or the fit-get-rich phase does not depend on the temperature or of the system but depends only on the functional form of the fitness distribution
\rho(η)